Summary of Pfme: a Modular Approach For Fine-grained Hallucination Detection and Editing Of Large Language Models, by Kunquan Deng et al.
PFME: A Modular Approach for Fine-grained Hallucination Detection and Editing of Large Language Models
by Kunquan Deng, Zeyu Huang, Chen Li, Chenghua Lin, Min Gao, Wenge Rong
First submitted to arxiv on: 29 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a standardized process for categorizing fine-grained hallucination types in Large Language Models (LLMs) and develops the Progressive Fine-grained Model Editor (PFME) to detect and correct such hallucinations. PFME consists of two modules: Real-time Fact Retrieval and Fine-grained Hallucination Detection and Editing. The former retrieves factual evidence from credible sources, while the latter identifies, locates, and edits sentence-level text based on relevant evidence and context. Experimental results show that PFME outperforms existing methods in fine-grained hallucination detection tasks using LLMs like Llama3-8B-Instruct, with an 8.7 percentage point improvement when assisted by external knowledge. In editing tasks, PFME enhances FActScore datasets for Alpaca13B and ChatGPT models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps improve Large Language Models (LLMs) by reducing inaccurate content called “hallucinations.” It creates a new way to find and fix these mistakes in LLMs. The method has two parts: one finds facts from trusted sources, and the other looks at sentences to see if they contain hallucinations and makes changes as needed. Tests show that this method works better than others for detecting fine-grained hallucinations. |
Keywords
» Artificial intelligence » Hallucination
